Characters matter: How narratives shape affective responses to risk communication

English version

Autoři: Elizabeth A. Shanahan ^aff001; Ann Marie Reinhold ^aff003; Eric D. Raile ^aff001; Geoffrey C. Poole ^aff002; Richard C. Ready ^aff002; Clemente Izurieta ^aff002; Jamie McEvoy ^aff002; Nicolas T. Bergmann ^aff006; Henry King ^aff005
Působiště autorů: Department of Political Science, College of Letters & Science, Montana State University, Bozeman, Montana, United States of America ^aff001; Montana Institute on Ecosystems, Montana State University, Bozeman, Montana, United States of America ^aff002; Department of Land Resources & Environmental Sciences, College of Agriculture, Montana State University, Bozeman, Montana, United States of America ^aff003; Department of Agricultural Economics & Economics, College of Agriculture, Montana State University, Bozeman, Montana, United States of America ^aff004; Department of Computer Science, Gianforte School of Computing, Montana State University, Bozeman, Montana, United States of America ^aff005; Department of Earth Sciences, College of Letters & Science, Montana State University, Bozeman, Montana, United States of America ^aff006
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0225968

Souhrn

Introduction

Whereas scientists depend on the language of probability to relay information about hazards, risk communication may be more effective when embedding scientific information in narratives. The persuasive power of narratives is theorized to reside, in part, in narrative transportation.

Purpose

This study seeks to advance the science of stories in risk communication by measuring real-time affective responses as a proxy indicator for narrative transportation during science messages that present scientific information in the context of narrative.

Methods

This study employed a within-subjects design in which participants (n = 90) were exposed to eight science messages regarding flood risk. Conventional science messages using probability and certainty language represented two conditions. The remaining six conditions were narrative science messages that embedded the two conventional science messages within three story forms that manipulated the narrative mechanism of character selection. Informed by the Narrative Policy Framework, the characters portrayed in the narrative science messages were hero, victim, and victim-to-hero. Natural language processing techniques were applied to identify and rank hero and victim vocabularies from 45 resident interviews conducted in the study area; the resulting classified vocabulary was used to build each of the three story types. Affective response data were collected over 12 group sessions across three flood-prone communities in Montana. Dial response technology was used to capture continuous, second-by-second recording of participants’ affective responses while listening to each of the eight science messages. Message order was randomized across sessions. ANOVA and three linear mixed-effects models were estimated to test our predictions.

Results

First, both probabilistic and certainty science language evoked negative affective responses with no statistical differences between them. Second, narrative science messages were associated with greater variance in affective responses than conventional science messages. Third, when characters are in action, variation in the narrative mechanism of character selection leads to significantly different affective responses. Hero and victim-to-hero characters elicit positive affective responses, while victim characters produce a slightly negative response.

Conclusions

In risk communication, characters matter in audience experience of narrative transportation as measured by affective responses.

Klíčová slova:

Communications – Flooding – Language – Rivers – Scientists – Semantics – Social communication – Transportation

Zdroje

1. Niles JD. Homo narrans: The poetics and anthropology of oral literature. Philadelphia: University of Pennsylvania Press; 1999.

2. Weedon C. Identity and culture: Narratives of difference and belonging. Allen S, editor. Berkshire, England: Open University Press McGraw-Hill Education; 2004.

3. Shanahan EA, McBeth MK, Hathaway PL. Narrative policy framework: The influence of media policy narratives on public opinion. Politics & Policy. 2011;39(3):373–400. doi: 10.1111/j.1747-1346.2011.00295.x

4. Green MC. Narratives and cancer communication. Journal of Communication. 2006;56(suppl_1):S163–S83. doi: 10.1111/j.1460-2466.2006.00288.x

5. Holmes FL. Scientific writing and scientific discovery. Isis. 1987;78(2):220–35. doi: 10.1086/354391 3316115

6. Fischhoff B. The sciences of science communication. Proceedings of the National Academy of Sciences. 2013;110(Supplement 3):14033–9. doi: 10.1073/pnas.1213273110 23942125

7. Jones M, Crow D. How can we use the ‘science of stories’ to produce persuasive scientific stories? Palgrave Communications. 2017;3(1):53. doi: 10.1057/s41599-017-0047-7

8. Jones MD, McBeth MK, Shanahan EA. Introducing the Narrative Policy Framework. The Science of Stories: Palgrave; 2014. p. 1–25.

9. De Wit JB, Das E, Vet R. What works best: Objective statistics or a personal testimonial? An assessment of the persuasive effects of different types of message evidence on risk perception. Health Psychology. 2008;27(1):110. doi: 10.1037/0278-6133.27.1.110 18230021

10. Shen F, Sheer VC, Li R. Impact of narratives on persuasion in health communication: A meta-analysis Journal of Advertising. 2015;44(2):105–13. doi: 10.1080/00913367.2015.1018467

11. Illari PM, Williamson J. What is a mechanism? Thinking about mechanisms across the sciences. European Journal for Philosophy of Science. 2012;2(1):119–35. doi: 10.1007/s13194-011-0038-2

12. Shanahan EA, Jones MD, McBeth MK, Radaelli C. The Narrative Policy Framework. In: Weible CM, Sabatier PA, editors. Theories of the Policy Process. 4th ed. Boulder, CO: Westview Press; 2018. p. 173–213.

13. Herman D. Basic elements of narrative. West Sussex, UK: John Wiley & Sons; 2011.

14. Shanahan EA, Raile ED, French KA, McEvoy J. Bounded stories. Policy Studies Journal. 2018;46(4):922–48. doi: 10.1111/psj.12269

15. Shanahan EA, Jones MD, McBeth MK, Lane RR. An angel on the wind: How heroic policy narratives shape policy realities. Policy Studies Journal. 2013;41(3):453–83. doi: 10.1111/psj.12025

16. Stone D. Policy paradox: The art of political decision making. 3rd Edition ed. New York, New York: W.W. Norton; 2012.

17. Jones MD. Cultural characters and climate change: How heroes shape our perception of climate science. Social Science Quarterly. 2014;95(1):1–39. doi: 10.1111/ssqu.12043

18. De Boer J, Wouter Botzen W, Terpstra T. Improving flood risk communication by focusing on prevention‐focused motivation. Risk Analysis. 2014;34(2):309–22. doi: 10.1111/risa.12091 23834075

19. Green MC, Brock TC. The role of transportation in the persuasiveness of public narratives. Journal of Personality and Social Psychology. 2000;79(5):701. doi: 10.1037//0022-3514.79.5.701 11079236

20. Green MC, Chatham C, Sestir MA. Emotion and transportation into fact and fiction. Scientific Study of Literature. 2012;2(1):37–59. doi: 10.1075/ssol.2.1.03gre

21. Appel M, Gnambs T, Richter T, Green MC. The transportation scale–short form (TS–SF). Media Psychology. 2015;18(2):243–66.

22. Sestir M, Green MC. You are who you watch: Identification and transportation effects on temporary self-concept. Social Influence. 2010;5(4):272–88. doi: 10.1080/15534510.2010.490672

23. Dransch D, Rotzoll H, Poser K. The contribution of maps to the challenges of risk communication to the public. International Journal of Digital Earth. 2010;3(3):292–311. doi: 10.1080/17538941003774668

24. Krewski D, Turner MC, Lemyre L, Lee JE. Expert vs. public perception of population health risks in Canada. Journal of Risk Research. 2012;15(6):601–25. doi: 10.1080/13669877.2011.649297

25. Cao Y, Boruff BJ, McNeill IM. Towards personalised public warnings: harnessing technological advancements to promote better individual decision-making in the face of disasters. International Journal of Digital Earth. 2017;10(12):1231–52. doi: 10.1080/17538947.2017.1302007

26. Paton D, Smith L, Daly M, Johnston D. Risk perception and volcanic hazard mitigation: Individual and social perspectives. Journal of Volcanology and Geothermal Research. 2008;172(3–4):179–88. doi: 10.1016/j.jvolgeores.2007.12.026

27. Pidgeon N, Kasperson RE, Slovic P. The social amplification of risk. Cambridge, UK: Cambridge University Press; 2003.

28. Wachinger G, Renn O, Begg C, Kuhlicke C. The risk perception paradox—implications for governance and communication of natural hazards. Risk Analysis. 2013;33(6):1049–65. doi: 10.1111/j.1539-6924.2012.01942.x 23278120

29. Kahan DM, Braman D. More statistics, less persuasion: A cultural theory of gun-risk perceptions. University of Pennsylvania Law Review. 2003;151(4):1291–327. doi: 10.2307/3312930

30. Jaeger C, Renn O, Rosa E, Webler T. Risk, uncertainty, and rational action. London: Routledge; 2001.

31. Hudson P, Botzen WW, Feyen L, Aerts JC. Incentivising flood risk adaptation through risk based insurance premiums: Trade-offs between affordability and risk reduction. Ecological Economics. 2016;125:1–13. doi: 10.1016/j.ecolecon.2016.01.015

32. Loewenstein G. Out of control: Visceral influences on behavior. Organizational Behavior and Human Decision Processes. 1996;65(3):272–92. doi: 10.1006/obhd.1996.0028

33. Loewenstein GF, Weber EU, Hsee CK, Welch N. Risk as feelings. Psychological Bulletin. 2001;127(2):267–86. doi: 10.1037/0033-2909.127.2.267 11316014

34. Peters E, Slovic P. The Role of affect and worldviews as orienting dispositions in the perception and acceptance of nuclear power. Journal of Applied Social Psychology. 1996;26(16):1427–53. doi: 10.1111/j.1559-1816.1996.tb00079.x

35. Slovic P, Flynn JH, Layman M. Perceived risk, trust, and the politics of nuclear waste. Science. 1991;254(5038):1603–7. doi: 10.1126/science.254.5038.1603 17782210

36. Slovic P, MacGregor DG, Peters E. Imagery, affect, and decision making. 1998.

37. Slovic P, Peters E, Finucane ML, MacGregor DG. Affect, risk, and decision making. Health Psychology. 2005;24(4):S35–S40. doi: 10.1037/0278-6133.24.4.S35

38. Slovic P. The feeling of risk: New perspectives on risk perception. New York, New York: Earthscan; 2010.

39. Slovic P, Finucane ML, Peters E, MacGregor DG. Risk as analysis and risk as feelings: Some thoughts about affect, reason, risk, and rationality. Risk Analysis. 2004;24(2):311–22. doi: 10.1111/j.0272-4332.2004.00433.x 15078302

40. Slovic P, Peters E. Risk perception and affect. Current Directions in Psychological Science. 2006;15(6):322–5. doi: 10.1111/j.1467-8721.2006.00461.x

41. Slovic P, Finucane ML, Peters E, MacGregor DGJEjoor. The affect heuristic. 2007;177(3):1333–52.

42. Slovic P, Peters E, Finucane ML, MacGregor DG. Affect, risk, and decision making. Health Psychology. 2005;24(4S):S35. doi: 10.1037/0278-6133.24.4.S35 16045417

43. McKaughan DJ, Elliott KC. Voles, vasopressin, and the ethics of framing. Science. 2012;338(6112):1285. doi: 10.1126/science.338.6112.1285-a 23224537

44. Sandman PM. Pandemics: Good hygiene is not enough. Nature. 2009;459(7245):322. doi: 10.1038/459322a 19458694

45. Sanquini AM, Thapaliya SM, Wood MM. A communications intervention to motivate disaster risk reduction. Disaster Prevention and Management. 2016;25(3):345–59. doi: 10.1108/DPM-11-2015-0256

46. Hinyard LJ, Kreuter MW. Using narrative communication as a tool for health behavior change: A conceptual, theoretical, and empirical overview. Health Education & Behavior. 2007;34(5):777–92. doi: 10.1177/1090198106291963 17200094

47. Janssen E, van Osch L, de Vries H, Lechner L. The influence of narrative risk communication on feelings of cancer risk. British Journal of Health Psychology. 2013;18(2):407–19. doi: 10.1111/j.2044-8287.2012.02098.x 23025465

48. Wesselmann ED, Wirth JH, Mroczek DK, Williams KD. Dial a feeling: Detecting moderation of affect decline during ostracism. Personality and individual differences. 2012;53(5):580–6. doi: 10.1016/j.paid.2012.04.039 23585705

49. Fraenkel L, Stolar M, Bates JR, Street RL Jr, Chowdhary H, Swift S, et al. Variability in affect and willingness to take medication. Medical Decision Making. 2018;38(1):34–43. doi: 10.1177/0272989X17727002 28853340

50. Gottman JM, Levenson RW. A valid procedure for obtaining self-report of affect in marital interaction. Journal of consulting and clinical psychology. 1985;53(2):151. doi: 10.1037//0022-006x.53.2.151 3998244

51. Demeritt D, Stephens EM, Créton-Cazanave L, Lutoff C, Ruin I, Nobert S. Communicating and using ensemble flood forecasts in flood incident management: Lessons from social science. Handbook of Hydrometeorological Ensemble Forecasting. 2019:1131–60. doi: 10.1007/978-3-642-40457-3_44-1

52. Lin II R-G. Californians need to be so afraid of a huge earthquake that they take action, scientists say. LA Times. 2017.

53. Jones L. The big ones: How natural disasters have shaped us (and what we can do about them): Icon Books; 2018.

54. Agency FEM. Percent annual chance data. Region 6: Risk Map Increasing Resilience Together; 2015.

55. Jones MD. Communicating Climate Change: Are Stories Better than ‘Just the Facts’? Policy Studies Journal. 2014;42(4):644–73. doi: 10.1111/psj.12072

56. Engineers USACo, Council YRCD. Yellowstone River cumulative effects analysis. Omaha, NE: U.S. Army Corps of Engineers Omaha District, 2015.

57. Bergmann NT, McEvoy J, Shanahan EA, Raile ED, Reinhold AM, Poole GC, et al. Thinking Through Levees: How Political Agency Extends Beyond the Human Mind. Annals of the American Association of Geographers. 2019:1–20. doi: 10.1080/24694452.2019.1655387

58. Engineers USACo. Flood insurance study: Yellowstone River and Tongue River at Miles City, Custer County Montana. Omaha, NE: U.S. Army Corps of Engineers Omaha District, 2007.

59. Engineers USACo. Section 22, Planning assistance to states study: Yellowstone River, City of Glendive/Dawson County, Montana. Omaha, NE: Army Corps of Engineers Omaha District, 2014.

60. Agency FEM. Flood or flooding. https://www.fema.gov/flood-or-flooding.

61. Benito G, Lang M, Barriendos M, Llasat MC, Francés F, Ouarda T, et al. Use of systematic, palaeoflood and historical data for the improvement of flood risk estimation. Review of scientific methods. Natural Hazards. 2004;31(3):623–43. doi: 10.1023/B:NHAZ.0000024895.48463.eb

62. QSR I. NVivo 11. 2015.

63. McHugh ML. Interrater reliability: The kappa statistic. Biochemia Medica. 2012;22(3):276–82. 23092060

64. Dialsmith. Perception analyzer http://www.dialsmith.com/.

65. Hackworth RS, Fredrickson WE. The effect of text translation on perceived musical tension in Debussy’s noel des enfants qui n’ont plus de maisons. Journal of Research in Music Education. 2010;58(2):184–95.

66. Team RDC. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna2018.

67. Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67(1):1–48. doi: 10.18637/jss.v067.i01

68. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software. 2017;82(13):1–26. doi: 10.18637/jss.v082.i13

69. Luke SG. Evaluating significance in linear mixed-effects models in R. Behavior Research Methods. 2017;49(4):1494–502. doi: 10.3758/s13428-016-0809-y 27620283

70. Lenth R, Singmann H, Love J, Buerkner P, Herve M. emmeans: Estimated Marginal Means 2019. https://cran.r-project.org/package=emmeans

71. Cook TD, Campbell DT. Validity. Chapter Two, in Quasi-Experimentation: Design and Analysis Issues for Field Settings, Boston: Houghton Mifflin. 1979:37–94.

72. Wohlin C, Runeson P, Höst M, Ohlsson MC, Regnell B, Wesslén A. Experimentation in software engineering: Springer Science & Business Media; 2012.

73. Barrett LF, Russell JA. The structure of current affect: Controversies and emerging consensus. Current directions in psychological science. 1999;8(1):10–4.

74. Nealis LJ, van Allen ZM, Zelenski JM. Positive affect and cognitive restoration: Investigating the role of valence and arousal. PloS one. 2016;11(1):e0147275. doi: 10.1371/journal.pone.0147275 26784026

75. Tsai JL, Levenson RW. Cultural influences on emotional responding: Chinese American and European American dating couples during interpersonal conflict. Journal of Cross-Cultural Psychology. 1997;28(5):600–25.

76. Hessler DM, Fainsilber Katz L. Children’s emotion regulation: Self-report and physiological response to peer provocation. Developmental Psychology. 2007;43(1):27. doi: 10.1037/0012-1649.43.1.27 17201506

77. Kodra E, Senechal T, McDuff D, El Kaliouby R, editors. From dials to facial coding: Automated detection of spontaneous facial expressions for media research. 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG); 2013: IEEE.

78. Olson R. Houston, we have a narrative: Why science needs story. Chicago, Ill: University of Chicago Press; 2015.

79. Hogan PC. Affective narratology: The emotional structure of stories. Herman D, editor. Lincoln, NE: University of Nebraska Press; 2011.

80. Kahneman D. Thinking, fast and slow. New York: Farrar, Straus, and Giroux; 2011.

81. Herman D. Cognitive narratology (revised version 22 Spetember 2013). In: Hühn P, editor. The living handbook of narratology. Hamburg, Germany: Hamburg University; 2013.

82. Alber J, Fludernik M. Postclassical narratology: approaches and analyses: The Ohio State University Press; 2010.

83. Nickerson RS. Confirmation bias: A ubiquitous phenomenon in many guises. Review of general psychology. 1998;2(2):175–220.

84. Taber CS, Lodge M. Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science. 2006;50(3):755–69.

85. Lodge M, Taber CS. The automaticity of affect for political leaders, groups, and issues: An experimental test of the hot cognition hypothesis. Political Psychology. 2005;26(3):455–82.

86. Thibodeau P, Peebles MM, Grodner DJ, Durgin FH. The Wished-For Always Wins Until the Winner Was Inevitable All Along: Motivated Reasoning and Belief Bias Regulate Emotion During Elections. Political Psychology. 2015;36(4):431–48.

87. Wohlin C, Runeson P, Höst M, Ohlsson MC, Regnell B, Wesslén A. Case Studies. Experimentation in Software Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. p. 55–72.

Characters matter: How narratives shape affective responses to risk communication

Souhrn

Introduction

Purpose

Methods

Results

Conclusions

Klíčová slova:

Zdroje

PLOS One

INSIGHTS from European Respiratory Congress

Současné pohledy na riziko v parodontologii

Svět praktické medicíny 3/2024 (znalostní test z časopisu)

Kardiologické projevy hypereozinofilií

Střevní příprava před kolonoskopií